QMIR 2026

Week 3: R Fundamentals – Data Types & Structures

Tristan Muno
February 19, 2026

Week 3 Goals

By the end of today, you should:

Understand the core concepts: objects, functions, and pipes
Know the main data types R uses to store information
Know the main data structures R uses to organize information
Be able to access and explore data using [] and $

Agenda

Leftovers from Last Week
Objects, Functions & Pipes
Data Types
Data Structures
Accessing & Exploring Data
Walkthrough & Exercises

Leftovers from Last Week

Cloning from GitHub
- This is how you access and submit the homeworks and take home exam
Creating and linking your private projects with GitHub
Habits for effective and efficient coding:
- learn to use shortcuts (e.g. opening and closing Positron panels)
- learn to use your keyboard (e.g. highlighting text, adding code cells)
- set theme and font that makes you feel good

Part I: Objects, Functions & Pipes

R as a Language for Storing and Transforming Information

To understand […] R, two slogans are helpful:

Everything that exists is an object

Everything that happens is a function call

– John Chambers

You give R objects – named containers holding information
You apply functions – operations that transform objects
You chain steps together using pipes

Objects – Storing Information

An object has:

a name (how you refer to it)
a value (the information stored)
a type (what kind of information)

An R example:

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# Assign a value to a name with <-
country <- "Germany"

gdp_per_capita <- 54300

is_eu_member <- TRUE

Objects – Storing Information

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# print by typing the name
country

[1] "Germany"

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gdp_per_capita

[1] 54300

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is_eu_member

[1] TRUE

Functions – Doing Things with Objects

A function:

takes one or more inputs (arguments)
does something
returns an output

function_name(argument1, argument2, ...)

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gdp_values <- c(54300, 43800, 33200)

# Functions transform objects
mean(gdp_values)

[1] 43766.67

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sqrt(gdp_per_capita)

[1] 233.0236

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nchar(country)

[1] 7

Functions – Doing Things with Objects

You can write your own functions.

Functions can be:

small personal helpers
reusable analysis tools
shared with others

👉 Collections of useful, published functions form packages. We will expore some next week.

Packages are what make R (and Python) powerful ecosystems.

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# A small custom helper
add_tax <- function(price) {
  price * 1.19
}

add_tax(10)

[1] 11.9

The Pipe – Chaining Operations

Without the pipe, nested calls become hard to read:

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round(mean(sqrt(gdp_values)), digits = 2)

[1] 208.17

With the pipe |>, read left to right – “and then”:

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gdp_values |> sqrt() |> mean() |> round(digits = 2)

[1] 208.17

The pipe says: take this, and then do that.

Comments – Code as Communication

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# BAD: comments that just repeat the code
x <- x + 1 # add 1 to x

# GOOD: comments that explain WHY
x <- x + 1 # index starts at 0, shift to 1-based for plotting

# Use comments to:
# - explain decisions ("why log-transform? right skew in raw data")
# - mark sections of your script
# - temporarily disable code during testing

Your most important collaborator is your future self — six months from now, you will not remember why you did what you did. Thanks to our Quarto literate programming workflow, you are living in the golden age of commenting – you can literally write full prose between code cells instead of apologizing in hashtags later.

Part II: Data Types

What Kind of Information Does R Store?

Type	Description	Example
`double`	Decimal numbers	`3.14`, `54300.5`
`integer`	Whole numbers	`42`, `2026`
`character`	Text / strings	`"Germany"`, `"hello"`
`logical`	True or false	`TRUE`, `FALSE`
`factor`	Categories	`"low"`, `"medium"`, `"high"`
`Date`	Calendar dates	`2026-02-23`

Numeric: Double & Integer

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gdp <- 54300.5 # double (default for numbers)
seats <- 736L # integer (the L suffix makes it explicit)

class(gdp)

[1] "numeric"

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class(seats)

[1] "integer"

For most purposes in this course, the integer/double distinction doesn’t matter – R handles it behind the scenes. But it’s good to know it exists.
It is important that you know how to find out if objects are numeric.

Character / String

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capital <- 'Berlin' # single or double quotes both work
country <- "Germany" # but double quotes are sometimes easier to see

nchar(country) # how many characters?

[1] 7

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toupper(country) # string manipulation

[1] "GERMANY"

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paste(country, "is in the EU")

[1] "Germany is in the EU"

Character / String

Numbers stored as characters are not numbers – R won’t do math with them:

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"42" + 1 # this does not work

Error in `"42" + 1`:
! non-numeric argument to binary operator

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as.numeric("42") + 1 # transforming the string to numeric makes it work

[1] 43

Logical / Boolean

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is_eu_member <- TRUE

is_eu_member

[1] TRUE

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population <- 84000000

# Comparisons produce logical values
population > 50000000

[1] TRUE

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population == 84000000

[1] TRUE

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!is_eu_member # NOT

[1] FALSE

Logicals are the backbone of filtering – you’ll use them constantly when subsetting data.

Logical Operators

Operator	Meaning	Example
`<`	Less than	`3 < 5`
`<=`	Less than or equal	`3 <= 3`
`>`	Greater than	`5 > 3`
`>=`	Greater than or equal	`5 >= 5`
`==`	Equal to	`3 == 3`
`!=`	Not equal to	`3 != 5`
`&`	AND (vectorised)	`(3 > 1) & (5 > 2)`
`\\|`	OR (vectorised)	`(3 > 5) \\| (5 > 2)`
`!`	NOT	`!(3 > 5)`
`%in%`	Is element contained in vector	`"Germany" %in% c("France", "Germany")`

Specialized Types: Factors & Dates

Factors – categorical variables with fixed levels

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regime <- factor(
  c("democracy", "autocracy", "democracy", "hybrid"),
  levels = c("autocracy", "hybrid", "democracy")
)

regime

[1] democracy autocracy democracy hybrid   
Levels: autocracy hybrid democracy

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levels(regime)

[1] "autocracy" "hybrid"    "democracy"

Dates

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today <- Sys.Date()
today

[1] "2026-02-19"

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treaty <- as.Date("1957-03-25") # Treaty of Rome
treaty

[1] "1957-03-25"

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today - treaty # date arithmetic!

Time difference of 25168 days

You’ll encounter these often in real data. The key insight: R treats them differently from plain text so it can do useful things like ordering, arithmetic, and plotting.

Part III: Data Structures

How Is Information Organized?

Structure	Dimensions	Types allowed
Vector	1D	One
Matrix	2D	One
Array	nD	One
Data frame	2D	Multiple
List	Any	Multiple

Vectors – The Building Block

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# c() combines values into a vector
countries <- c("Germany", "France", "Italy", "Spain", "Turkey")
population <- c(84, 68, 60, 47, 88) # millions
eu_member <- c(TRUE, TRUE, TRUE, TRUE, FALSE)

length(countries) # number of elements in vector

[1] 5

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class(countries) # check type

[1] "character"

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class(eu_member) # check type

[1] "logical"

Vectors – The Building Block

A vector can only hold one type. If you mix types, R silently converts:

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mixed <- c(1, 2, "three") # all become character!
mixed

[1] "1"     "2"     "three"

Matrices & Arrays — Briefly

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m <- matrix(1:6, nrow = 2, ncol = 3)
m

     [,1] [,2] [,3]
[1,]    1    3    5
[2,]    2    4    6

2D, one type
Used for linear algebra behind the scenes
In practice: once you meet data frames, you’ll rarely use matrices directly

Data Frames – The Workhorse

Rectangular (rows × columns)
Each column is a vector (one type)
Columns can be different types from each other
Each row is an observation

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eu_data <- data.frame(
  country = c("Germany", "France", "Italy", "Spain", "Turkey"),
  population = c(84, 68, 60, 47, 88),
  eu_member = c(TRUE, TRUE, TRUE, TRUE, FALSE),
  regime = c("democracy", "democracy", "democracy", "democracy", "hybrid")
)

eu_data

  country population eu_member    regime
1 Germany         84      TRUE democracy
2  France         68      TRUE democracy
3   Italy         60      TRUE democracy
4   Spain         47      TRUE democracy
5  Turkey         88     FALSE    hybrid

Data Frames – The Workhorse

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nrow(eu_data) # number of rows (observations)

[1] 5

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ncol(eu_data) # number of columns (variables)

[1] 4

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head(eu_data) # first 5 rows of df

  country population eu_member    regime
1 Germany         84      TRUE democracy
2  France         68      TRUE democracy
3   Italy         60      TRUE democracy
4   Spain         47      TRUE democracy
5  Turkey         88     FALSE    hybrid

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str(eu_data)

'data.frame':   5 obs. of  4 variables:
 $ country   : chr  "Germany" "France" "Italy" "Spain" ...
 $ population: num  84 68 60 47 88
 $ eu_member : logi  TRUE TRUE TRUE TRUE FALSE
 $ regime    : chr  "democracy" "democracy" "democracy" "democracy" ...

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summary(eu_data)

   country            population   eu_member          regime         
 Length:5           Min.   :47.0   Mode :logical   Length:5          
 Class :character   1st Qu.:60.0   FALSE:1         Class :character  
 Mode  :character   Median :68.0   TRUE :4         Mode  :character  
                    Mean   :69.4                                     
                    3rd Qu.:84.0                                     
                    Max.   :88.0

Lists – The Flexible Container

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country_info <- list(
  name = "Germany",
  population = 84000000,
  eu_member = TRUE,
  parties = c("SPD", "CDU", "Greens", "FDP")
)

country_info

$name
[1] "Germany"

$population
[1] 8.4e+07

$eu_member
[1] TRUE

$parties
[1] "SPD"    "CDU"    "Greens" "FDP"

Lists can hold anything – including other lists. They’re less intuitive than data frames, but you’ll encounter them constantly as outputs of statistical models.

Part IV: Accessing & Exploring Data

Reaching Inside Structures

Two main tools:

[ ] – index by position or condition
$ – index by name (for data frames and lists)

Indexing Vectors with `[ ]`

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countries <- c("Germany", "France", "Italy", "Spain", "Poland")

countries[1] # first element

[1] "Germany"

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countries[c(1, 3)] # first and third

[1] "Germany" "Italy"

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countries[-2] # everything except second

[1] "Germany" "Italy"   "Spain"   "Poland"

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population <- c(84, 68, 60, 47, 38)

# Logical indexing — filter by condition
countries[population > 50]

[1] "Germany" "France"  "Italy"

Indexing DFs with `[ ]` and `$`

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# [row, column] -- leave blank to mean "all"
eu_data[1, ] # first row, all columns

  country population eu_member    regime
1 Germany         84      TRUE democracy

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eu_data[, 2] # all rows, second column

[1] 84 68 60 47 88

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eu_data[1, 2] # specific cell

[1] 84

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# $ accesses a column by name -- returns a vector
eu_data$country

[1] "Germany" "France"  "Italy"   "Spain"   "Turkey"

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eu_data$population

[1] 84 68 60 47 88

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# Combine: filter rows by condition
eu_data[eu_data$population > 65, ]

  country population eu_member    regime
1 Germany         84      TRUE democracy
2  France         68      TRUE democracy
5  Turkey         88     FALSE    hybrid

Exploring Data – Your First Questions

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str(eu_data) # structure: types, dimensions, preview

'data.frame':   5 obs. of  4 variables:
 $ country   : chr  "Germany" "France" "Italy" "Spain" ...
 $ population: num  84 68 60 47 88
 $ eu_member : logi  TRUE TRUE TRUE TRUE FALSE
 $ regime    : chr  "democracy" "democracy" "democracy" "democracy" ...

Exploring Data – Your First Questions

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summary(eu_data) # summary statistics per column

   country            population   eu_member          regime         
 Length:5           Min.   :47.0   Mode :logical   Length:5          
 Class :character   1st Qu.:60.0   FALSE:1         Class :character  
 Mode  :character   Median :68.0   TRUE :4         Mode  :character  
                    Mean   :69.4                                     
                    3rd Qu.:84.0                                     
                    Max.   :88.0

Exploring Data – Your First Questions

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head(eu_data, 2) # first n rows

  country population eu_member    regime
1 Germany         84      TRUE democracy
2  France         68      TRUE democracy

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class(eu_data) # what kind of object is this?

[1] "data.frame"

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class(eu_data$population) # what class/type is this variable?

[1] "numeric"

These are some of the first things you should run whenever you encounter a new dataset.

Time to Practice!

Walkthrough Overview

Clone the Week 3 exercise repo from GitHub (same workflow as last week)
Open exercise_week03.qmd in Positron
Work through the tasks in order
Fast finishers: optional depth exercises at the bottom of the document

Exercise Structure

Core tasks (everyone):

Create vectors of different types and check their class
Build a small data frame from scratch – use a political science example
Subset with [ ] and $
Write clean, commented code throughout

Optional depth exercises (fast movers):

Working with factors: level ordering and table()
Date arithmetic with as.Date() and lubridate

Homework

Distributed via GitHub (same workflow as Week 2)
Submit a rendered Quarto document (.qmd + rendered PDF)
Due: one week from today

The homework follows the same arc as today – you’ll work with a small political dataset to practice types, structures, and subsetting. Read the task descriptions carefully and write comments explaining your reasoning.

Thank you for your attention and see you next week!

QMIR 2026

Week 3: R Fundamentals – Data Types & Structures

Week 3 Goals

Agenda

Leftovers from Last Week